China’s reliance on imports for the most sophisticated scientific instruments could hold back the country’s use of AI in science, according to a leading Chinese researcher.
Advanced equipment such as mass spectrometers was essential for generating the high-quality experimental data needed to develop, validate and improve advanced scientific models, Weinan E, a professor at Peking University’s mathematical sciences school, said at the “AI for Science” conference in Shanghai last week.
“Without domestically developed precision instruments, it becomes difficult to obtain first-hand, high-quality experimental data, leaving AI ‘like cooking without rice’,” E said, according to Shanghai-based news outlet The Paper last week.
E, who is also a member of the Chinese Academy of Sciences, proposed the concept of “AI for Science” in 2018 as a new approach to research.
Scientists use artificial intelligence tools to improve scientific research, in areas ranging from improving computational modelling to experimental design.
And to get the best data, researchers need the best equipment.
Despite rapid market growth and government support for technological self-reliance, China has long relied heavily on imports for advanced scientific instruments, particularly from the United States, which leads at the high end of the industry.
In 2024, China imported nearly US$17 billion in scientific equipment, and more than three-quarters of the big research instruments used in the country were from abroad, according to a report in December by Beijing-based consulting firm Puhua Policy.
And in a report earlier this year, consultancy firm LeadLeo estimated that China relied on imports for 83 per cent of its mass spectrometers and chromatographs, and 75 per cent of its spectrometers.
These instruments are essential for scientific research. Mass spectrometers help identify molecules, chromatographs separate chemicals for analysis, and spectrometers use light to study the properties of materials.
China was also almost completely dependent on imports for optical instruments and biological tissue analysis equipment, the LeadLeo report said.
The dependence has led to high equipment costs, long maintenance cycles, and slower after-sales support, raising concerns over China’s research efficiency and supply-chain resilience.
At the same time, the US has restricted Chinese access to this kind of equipment.
By December 2020, during Donald Trump’s first term as US president, more than 42 per cent of the 4,510 China-related entries on the US export control list involved scientific instruments, according to analysis by Chinese researchers.
Those efforts have continued into Trump’s second term, driven by concerns in Washington that advanced technologies could support Beijing’s military modernisation and help design new weapons through AI.
In January, the US Department of Commerce announced new export controls involving high-parameter flow cytometers and certain mass spectrometry equipment, saying that the technologies could “generate high-quality, high-content biological data, including that which is suitable for use to facilitate the development of AI and biological design tools”.
In his address to the conference in Shanghai last week, E also warned that China’s progress in AI for science could be constrained by the “significant gaps” in foundation models compared to their international counterparts, calling it a top risk that “cannot be overlooked” and “a reality that must be confronted”.
He said that simply adding scientific capabilities to existing open-source models had proved “a false premise”, as solving complex scientific problems required stronger underlying models rather than post-training modifications alone.
He said the big difference was in the way the US and China equipped AI with scientific abilities.
While the US concentrated on improving general-purpose foundation models and integrating them with automated research infrastructure, China had adopted a more application-driven approach.
He said China started by building scientific AI infrastructure that integrated data, software, computing resources and automated equipment, and then applied these capabilities to specific research fields and scientific tasks.
E called for a restructuring of China’s research system to adapt to the AI era, identifying three “breaks”.
He said the scientific community should break down boundaries between disciplines to enable cross-field research; break the divide between theory and experimentation; and break down the barrier between academia and industry.
He also suggested an overhaul of traditional research evaluation systems, calling for greater recognition of contributions beyond academic publications, such as the development of data, software and research infrastructure. -- SOUTH CHINA MORNING POST
